The goal of the journal is to be an outlet for high quality theoretical and applied research on hybrid, knowledge-driven computational approaches that may be characterized under any of the following categories of memetics:

  • Type 1: General-purpose algorithms integrated with human-crafted heuristics that capture some form of prior domain knowledge; e.g., traditional memetic algorithms hybridizing evolutionary global search with a problem-specific local search. The journal welcomes investigations into various modes of meme transmission. Demonstrations of memetics in the context of deep neuroevolution, synergizing evolutionary search of neural architectures with lifetime learning of specific tasks or sets of tasks, are of significant interest.
  • Type 2: Algorithms with the ability to automatically select, adapt, and reuse the most appropriate heuristics from a diverse pool of available choices; e.g., learning a mapping between global search operators and multiple local search schemes, given an optimization problem at hand.
  • Type 3: Algorithms that autonomously learn with experience, adaptively reusing data and/or machine learning models drawn from related problems as prior knowledge in new target tasks of interest; examples include, but are not limited to, transfer learning and optimization, multi-task learning and optimization, or any other multi-X evolutionary learning and optimization methodologies.

Authors are encouraged to submit original research articles, including reviews and short communications, expanding the conceptual scope of memetics (e.g., to Type-X and beyond) and/or advancing the algorithmic state-of-the-art. Articles reporting novel real-world applications of memetics in areas including, but not limited to, multi-X evolutionary computation, neuroevolution, embodied cognition and intelligence of autonomous agents, continuous and discrete optimization, knowledge-guided machine learning, computationally expensive search problems, shall be considered for publication.

  • Features high quality research in hybrid metaheuristics (including evolutionary hybrids) for optimization, control and design in continuous and discrete optimization domains
  • Goes beyond current search methodologies towards innovative research on the emergence of cultural artifacts
  • Presents the latest results which are fuzzed together in novel ways in order to transcend the intrinsic limitations of a single discipline

Journal information

  • Chuan-Kang Ting
Publishing model
Hybrid (Transformative Journal). Learn about publishing Open Access with us

Journal metrics

5.900 (2020)
Impact factor
4.368 (2020)
Five year impact factor
76 days
Submission to first decision
297 days
Submission to acceptance
18,264 (2020)

Latest issue

Volume 13

Issue 2, June 2021

Thematic Issue on Memetic Algorithms with Learning Strategy

Latest articles

This journal has 5 open access articles

Journal updates

View all updates


Note this is only the net price. Taxes will be calculated during checkout.
  • Immediate online access with complete access to all articles starting 1997
  • Downloadable in PDF format
  • Subscription expires 12/31/2021

About this journal

Electronic ISSN
Print ISSN
Abstracted and indexed in
  1. ACM Digital Library
  2. CNKI
  3. Current Contents/Engineering, Computing and Technology
  4. DBLP
  5. Dimensions
  6. EBSCO Discovery Service
  7. EI Compendex
  8. Google Scholar
  10. Institute of Scientific and Technical Information of China
  11. Japanese Science and Technology Agency (JST)
  12. Journal Citation Reports/Science Edition
  13. Naver
  14. Norwegian Register for Scientific Journals and Series
  15. OCLC WorldCat Discovery Service
  16. ProQuest Advanced Technologies & Aerospace Database
  17. ProQuest Central
  18. ProQuest SciTech Premium Collection
  19. ProQuest Technology Collection
  20. ProQuest-ExLibris Primo
  21. ProQuest-ExLibris Summon
  22. SCImago
  23. SCOPUS
  24. Science Citation Index Expanded (SciSearch)
  25. TD Net Discovery Service
  26. UGC-CARE List (India)
Copyright information

Rights and permissions

Springer policies

© Springer-Verlag GmbH Germany, part of Springer Nature